Artificial intelligence (AI) is generating a lot of interest from digital marketers. And for good reason — in customer experience personalization, it has huge potential to maximize conversion lift, remove human bias, incorporate visitor context, and eliminate the need for manual data analysis. A recent BrightEdge survey revealed that 32 percent of respondents view artificial intelligence (AI) as the “next big thing.” I couldn’t agree more. But what might encourage marketers to dive into using it more?

In this post, I’d like to introduce two new reports in Adobe Target that we’re releasing in beta and rolling out to participating customers now, and that will be available to all Target customers this spring. These reports show how we’re helping you get even more out of the AI and machine learning capabilities in Adobe Target, powered by Adobe Sensei. You’ll quickly see that the reports offer benefits well beyond conversion rate increases, so that you can start using the AI-driven personalization from Adobe Sensei in Adobe Target even more.

Tapping into the thought process of the machine learning algorithm.

Many of you have been using Auto-Target and Automated Personalization, two of the AI and machine learning capabilities available in Adobe Target. The algorithms beneath these capabilities use all available profile data for each visitor — first party data like geolocation and behavioral variables, as well as any customer record (CRM) or third-party data that regularly feeds into the solution. It then learns what attributes best predict conversion for each experience. The algorithms are driving wildly successful conversion lifts. For example, one customer who heavily uses Auto-Target regularly sees 20 percent to 80 percent conversion rate increases. That’s awesome. But what can you learn from those wins (and any losses) to inform your next marketing campaign?

Currently, you review the Insights reports associated with your AI-driven activities in Target. These reports show you how each experience performed and indicate which individual profile attributes most influenced the algorithm’s decision to deliver a specific experience. But what if you could go a level deeper with these insights? What if you had more visibility into why the algorithm made the decisions it did and could understand things like which audiences or combinations of profile attributes drove the algorithm to deliver a given experience?

Now you can.

New Insights reports offer visibility you can act on.

Adobe Target is adding two new tabs to the Insights reports of your Auto-Target and Automated Personalization activities. These tabs give you access to two new tabular reports that become available once your algorithm has finished pre-processing the training data and is actively delivering personalized content. As the algorithm self-optimizes over time, these reports update to reflect changes due to seasonality, trends, and other factors. The new Insights reports include:

Automated Segment InsightsThis report shows the top 10 audiences of a certain size threshold that the model identifies from the data as responding differently to your various offers, along with the offers to which they responded. The report answers the key question, “Which audiences most resonated with my various offers, and with which specific offers did each resonate?” For example, the report might reveal that city-dwelling millennials responded best to your 20 percent off and free shipping offers, while suburban baby boomers preferred offers that let them apply their loyalty points to purchases. That type of information provides clear direction on the types of offers to create and deliver to each audience in the future.

Model Attribute RankingThis report rates all influencers of the model by their contribution to the model. Again, an influencer can be a first-party customer attribute, second-party data like that from your CRM system, or third-party purchased data. This report answers the question, “What individual profile attributes does my machine learning algorithm view as most influential in its decision to deliver a specific experience?” It could show you that a model input you thought was unimportant, like number of years as a customer from your CRM, actually drove a significant contribution to the model. This learning can steer you toward developing experiences and content based on this attribute.

More features on the horizon to unleash the power of AI.

With these new reports, Adobe Target essentially is serving up valuable insights on a platter — you just have to unfold your napkin, tuck it in, and enjoy. In the future, look for features that let you pull more levers of the algorithm, but with safeguards in place so that you don’t pull those levers so far that you lose all the goodness the algorithm has to offer. These types of new features reveal the path we’re on to give you more control of the algorithm and transparency into how it works — all with the ultimate goal of letting you fully unleash the power of AI in Adobe Target for personalizing at scale.